2.1. Covariation

Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.

⚠️ Only linear models are implemented now, as there are some bugs with the automatized calculation of the others.

2.2. Correlation

Correlations have been calculated with Spearman’s rank coefficient.

3.1. Taxa identity

The most abundant taxa in our study area are:

• Density: B.neotena (1969), E. integra (1158), P.grandimana (1092), Nematoda (1044) and M. calcarea (575)
• Biomass: E. parma (biomass of 531.5), Strongylocentrotus sp. (65.3), N. incisa (58.5), M. calcarea (45.4) and S. groenlandicus (34.3)

The following graphs present the distribution of sampled phyla along index of cumulative exposure, according to density (left side) or biomass (right side).

Exposure categories are based on the exposure index: the higher the index, the lower the status. Five exposure categories (‘bad’, ‘poor’, ‘moderate’, good’, ‘high’) have been set relative to the exposure index (5.6 ≤ \(E\), 4.2 ≤ \(E\) < 5.6, 2.8 ≤ \(E\) < 4.2, 1.4 ≤ \(E\) < 2.8, \(E\) < 1.4, respectively). Maximum detected cumulative exposure is 3.698.

3.2. Community characteristics

The following graphs present the distribution of community characteristics along index of cumulative exposure.

Exposure categories are based on the exposure index: the higher the index, the lower the status. Maximum cumulative exposure is 3.698. Five exposure categories from ‘bad’ to ‘high’ have been set with 20 %, 40 %, 60 % or 80 % of the maximum exposure.

4.1. Data manipulation

All stations and predictors were selected for the regressions, as we are interested in each of them (following graphs are for information only).

4.2. Univariate regressions

We used linear models for the regressions on community characteristics. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models). Variable selection was not needed here, as we are interested in all exposure indices.

Results of regressions (coefficients with a significant p-value for marginal tests) are shown below. Using both abiotic parameters and exposure indices as predictors do not increase significantly predictive power compared to the other models. Details of the regressions, with diagnostics and cross-validation, are summarized below.

## Adjusted R2 is: 0.23

## RMSE from cross-validation: 1.026268

## Adjusted R2 is: 0.02

## RMSE from cross-validation: 1.118892

## Adjusted R2 is: 0.2

## RMSE from cross-validation: 1.172694

## Adjusted R2 is: 0.33

## RMSE from cross-validation: 1.057078

## Adjusted R2 is: 0.12

## RMSE from cross-validation: 1.13629

## Adjusted R2 is: 0.18

## RMSE from cross-validation: 1.006118

## Adjusted R2 is: 0.06

## RMSE from cross-validation: 1.033773

## Adjusted R2 is: 0.18

## RMSE from cross-validation: 1.179323

## Adjusted R2 is: 0.32

## RMSE from cross-validation: 0.9988091

## Adjusted R2 is: 0.12

## RMSE from cross-validation: 1.061198

## Adjusted R2 is: 0.16

## RMSE from cross-validation: 0.945198

## Adjusted R2 is: 0.02

## RMSE from cross-validation: 1.06337

## Adjusted R2 is: 0.04

## RMSE from cross-validation: 1.018014

## Adjusted R2 is: 0.29

## RMSE from cross-validation: 0.889925

## Adjusted R2 is: 0.14

## RMSE from cross-validation: 0.986413

4.3. Multivariate regression

The model selected by the DistLM procedure has a \(R^{2}\) of 0.22 (results extracted form PRIMER-e). Colours represent the value of the cumulative exposure index (the bluer, the higher).